An Empirical Comparison of Genetically Evolved Programs and Evolved Neural Networks for Multi-agent Systems Operating under Dynamic Environments

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Davila:2015:GECCOcomp,

• author = "Jaime J. Davila",
• title = "An Empirical Comparison of Genetically Evolved Programs and Evolved Neural Networks for Multi-agent Systems Operating under Dynamic Environments",
• booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation",
• year = "2015",
• editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr",
• isbn13 = "978-1-4503-3488-4",
• keywords = "genetic algorithms, genetic programming: Poster",
• pages = "1373--1374",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• URL = "http://doi.acm.org/10.1145/2739482.2764717",
• DOI = "doi:10.1145/2739482.2764717",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "This paper expands on the research presented in [12] by comparing the performance of genetically evolved programs operating under dynamic game environments with that of neural networks with evolved weights. On the genetic programming side, the maximum allowed tree depth was varied in order to study its effect on the evolutionary process. For evolution of neural networks, encoding included direct encoding of weights and three different L-Systems. Empirical results show that genetic evolution of neural networks weights provided better performance under dynamic environments when evolved to choose which of several high-level actions to perform, such as defend or attack. On the other hand, genetic programming evolved better solutions for low-level actions, such as move left, move right, or accelerate. Solutions are analysed in order to explain these differences.",
• notes = "Also known as \cite{2764717} Distributed at GECCO-2015.",

}

Genetic Programming entries for Jaime J Davila

Citations